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| import torch | |
| import torch.nn as nn | |
| from torchvision import models, transforms | |
| from PIL import Image | |
| import gradio as gr | |
| # 1. Config | |
| # We assume the model file is in the same folder as this script for the web deployment | |
| MODEL_PATH = "efficientnet_b0_best.pth" | |
| CLASSES = ['REAL', 'FAKE'] | |
| # 2. Load Model | |
| def load_model(): | |
| # Define the Architecture (must match your training exactly) | |
| model = models.efficientnet_b0(weights=None) | |
| num_ftrs = model.classifier[1].in_features | |
| model.classifier[1] = nn.Linear(num_ftrs, 2) | |
| # Load Weights (map_location=cpu is important for free web tiers) | |
| model.load_state_dict(torch.load(MODEL_PATH, map_location=torch.device('cpu'))) | |
| model.eval() | |
| return model | |
| model = load_model() | |
| # 3. Preprocessing (Must match training transforms) | |
| transform = transforms.Compose([ | |
| transforms.Resize((224, 224)), | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| # 4. Prediction Function | |
| def predict(image): | |
| if image is None: | |
| return None | |
| try: | |
| image_tensor = transform(image).unsqueeze(0) | |
| with torch.no_grad(): | |
| outputs = model(image_tensor) | |
| probabilities = torch.nn.functional.softmax(outputs[0], dim=0) | |
| return {CLASSES[0]: float(probabilities[0]), CLASSES[1]: float(probabilities[1])} | |
| except Exception as e: | |
| return f"Error: {e}" | |
| # 5. The Web Interface | |
| interface = gr.Interface( | |
| fn=predict, | |
| inputs=gr.Image(type="pil", label="Upload Image"), | |
| outputs=gr.Label(num_top_classes=2, label="Result"), | |
| title="Real vs AI Image Detector", | |
| description="Is this image Real or AI-Generated? Upload it to find out." | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch() |